The performance of rainwater harvesting systems in the context of deep uncertainties

Abstract

Rainwater harvesting systems (RHS) are a relevant alternative of water supply in urban areas with increasing water demand and limited water availability. But these systems depend on several parameters that present uncertainties as well-characterized uncertainties whose probability functions are known and deep uncertainty factors that doesn't have analytical representation of their variability. This study evaluates the influence of water demand, tariff and discount rate (deep uncertain factors) on the feasibility of RHS for different scenarios of uncertainties. The systems were evaluated using the following performance criteria: Satisfied Demand, Reliability, Percentage of Rainwater Harvesting, Net Present Value, Net Present Value Volume and Benefit Cost Rate. We simulated the RHS performance for sixteen system configurations, comprised of eight categories of residential buildings according to representative water consumption (ranging from 4.748 to 44.673 m3 per month) and two typical catchment areas for each of the eight groups of demands (ranging from 60 to 400 m2) in the city of Rio Verde located in the central of Brazil. Each system was evaluated under the context of 1000 States of the World (SOWs) defined using the Latin Hypercube Sampling (LHS) method (in the case of the deep uncertainty factors) and bootstrapping resampling (for precipitation). Results showed slight difference on performance criteria among precipitation scenarios, maybe due to the fact that the synthetic rainfall series preserved the pattern and the total rainfall volume among the series which is reasonable for the location. However, the water tariff and discount rate showed a significant influence in the performance criteria confirming the relevance of deep uncertainty factors in the evaluation of RHS performances.